Several methods such as CTAP [1]}, MGG [2]}, PBRNet [3]}, RapNet [4]} balance the advantages and disadvantages between anchor-based and anchor-free approaches for proposal generation. CTAP [1]} designed a complementary filter applied on the initial proposals to generate the probabilities of proposal detection by anchor-free TAG [6]} (defined in REF ). The original use of complementary filtering is to estimate a signal given two noisy measurements, where one of them is mostly high-frequency (maybe precise but not stable) similar to TAG proposals and the other one is mostly low-frequency (stable but not precise) similar to sliding-window proposals. Also, several temporal feature pyramid networks (TFPN, defined in REF ) such as MGG [2]}, PBRNet  [3]}, and RapNet  [4]} generate coarse segment proposals of various length with TFPN (anchor-based), and simultaneously predict fine-level frame actionness (anchor-free). The advantage of this idea is to adjust the segment boundary of proposals with frame actionness information during the inference.
